
"""simple docstring"""






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
					import transformers




def __magic_name__	(				_lowerCamelCase:		int			)					->						List[str]:

	'''simple docstring'''




	random.seed(_lowerCamelCase			)
	np.random.seed(_lowerCamelCase			)
	torch.manual_seed(_lowerCamelCase			)
	torch.cuda.manual_seed_all(_lowerCamelCase			)
	# ^^ safe to call this function even if cuda is not available



class 			lowercase					:






			def __init__(self  :	Dict      ,SCREAMING_SNAKE_CASE_  :	Iterable[torch.nn.Parameter]      ,SCREAMING_SNAKE_CASE_  :	float = 0.99_99      ,SCREAMING_SNAKE_CASE_  :	float = 0.0      ,SCREAMING_SNAKE_CASE_  :	int = 0      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	Union[float, int] = 1.0      ,SCREAMING_SNAKE_CASE_  :	Union[float, int] = 2 / 3      ,SCREAMING_SNAKE_CASE_  :	Optional[Any] = None      ,SCREAMING_SNAKE_CASE_  :	Dict[str, Any] = None      ,**SCREAMING_SNAKE_CASE_  :	str      ,)       ->	Optional[Any]:





				"""simple docstring"""


				if isinstance(SCREAMING_SNAKE_CASE_      ,torch.nn.Module			):
					lowerCAmelCase												=  (
					    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
					    '''Please pass the parameters of the module instead.'''
					)
					deprecate(
					    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''      ,'''1.0.0'''      ,SCREAMING_SNAKE_CASE_      ,standard_warn=SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  parameters.parameters()

					# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
					lowerCAmelCase												=  True

				if kwargs.get('''max_value'''      ,SCREAMING_SNAKE_CASE_			) is not None:
					lowerCAmelCase												=  '''The `max_value` argument is deprecated. Please use `decay` instead.'''
					deprecate('''max_value'''      ,'''1.0.0'''      ,SCREAMING_SNAKE_CASE_      ,standard_warn=SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  kwargs['''max_value''']

				if kwargs.get('''min_value'''      ,SCREAMING_SNAKE_CASE_			) is not None:
					lowerCAmelCase												=  '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
					deprecate('''min_value'''      ,'''1.0.0'''      ,SCREAMING_SNAKE_CASE_      ,standard_warn=SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  kwargs['''min_value''']

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  [p.clone().detach() for p in parameters]

				if kwargs.get('''device'''      ,SCREAMING_SNAKE_CASE_			) is not None:
					lowerCAmelCase												=  '''The `device` argument is deprecated. Please use `to` instead.'''
					deprecate('''device'''      ,'''1.0.0'''      ,SCREAMING_SNAKE_CASE_      ,standard_warn=SCREAMING_SNAKE_CASE_			)
					self.to(device=kwargs['''device''']			)

				lowerCAmelCase												=  None

				lowerCAmelCase												=  decay
				lowerCAmelCase												=  min_decay
				lowerCAmelCase												=  update_after_step
				lowerCAmelCase												=  use_ema_warmup
				lowerCAmelCase												=  inv_gamma
				lowerCAmelCase												=  power
				lowerCAmelCase												=  0
				lowerCAmelCase												=  None  # set in `step()`

				lowerCAmelCase												=  model_cls
				lowerCAmelCase												=  model_config






			@classmethod
			def 					UpperCAmelCase							(cls  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	Any			)       ->	"EMAModel":





				"""simple docstring"""


				lowerCAmelCase							,       lowerCAmelCase												=  model_cls.load_config(SCREAMING_SNAKE_CASE_      ,return_unused_kwargs=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  model_cls.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  cls(model.parameters()      ,model_cls=SCREAMING_SNAKE_CASE_      ,model_config=model.config			)

				ema_model.load_state_dict(SCREAMING_SNAKE_CASE_			)
				return ema_model






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	List[Any]:





				"""simple docstring"""


				if self.model_cls is None:
					raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''			)

				if self.model_config is None:
					raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''			)

				lowerCAmelCase												=  self.model_cls.from_config(self.model_config			)
				lowerCAmelCase												=  self.state_dict()
				state_dict.pop('''shadow_params'''      ,SCREAMING_SNAKE_CASE_			)

				model.register_to_config(**SCREAMING_SNAKE_CASE_			)
				self.copy_to(model.parameters()			)
				model.save_pretrained(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	float:





				"""simple docstring"""


				lowerCAmelCase												=  max(0      ,optimization_step - self.update_after_step - 1			)

				if step <= 0:
					return 0.0

				if self.use_ema_warmup:
					lowerCAmelCase												=  1 - (1 + step / self.inv_gamma) ** -self.power
				else:
					lowerCAmelCase												=  (1 + step) / (10 + step)

				lowerCAmelCase												=  min(SCREAMING_SNAKE_CASE_      ,self.decay			)
				# make sure decay is not smaller than min_decay
				lowerCAmelCase												=  max(SCREAMING_SNAKE_CASE_      ,self.min_decay			)
				return cur_decay_value






			@torch.no_grad()
			def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	Iterable[torch.nn.Parameter]			)       ->	Tuple:





				"""simple docstring"""


				if isinstance(SCREAMING_SNAKE_CASE_      ,torch.nn.Module			):
					lowerCAmelCase												=  (
					    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
					    '''Please pass the parameters of the module instead.'''
					)
					deprecate(
					    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''      ,'''1.0.0'''      ,SCREAMING_SNAKE_CASE_      ,standard_warn=SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  parameters.parameters()

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)

				self.optimization_step += 1

				# Compute the decay factor for the exponential moving average.
				lowerCAmelCase												=  self.get_decay(self.optimization_step			)
				lowerCAmelCase												=  decay
				lowerCAmelCase												=  1 - decay

				lowerCAmelCase												=  contextlib.nullcontext
				if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
					import deepspeed

				for s_param, param in zip(self.shadow_params      ,SCREAMING_SNAKE_CASE_			):
					if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
						lowerCAmelCase												=  deepspeed.zero.GatheredParameters(SCREAMING_SNAKE_CASE_      ,modifier_rank=SCREAMING_SNAKE_CASE_			)

					with context_manager():
						if param.requires_grad:
							s_param.sub_(one_minus_decay * (s_param - param)			)
						else:
							s_param.copy_(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Iterable[torch.nn.Parameter]			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)
				for s_param, param in zip(self.shadow_params      ,SCREAMING_SNAKE_CASE_			):
					param.data.copy_(s_param.to(param.device			).data			)






			def 					UpperCAmelCase							(self  :	Dict      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	Tuple=None			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  [
				    p.to(device=SCREAMING_SNAKE_CASE_      ,dtype=SCREAMING_SNAKE_CASE_			) if p.is_floating_point() else p.to(device=SCREAMING_SNAKE_CASE_			)
				    for p in self.shadow_params
				]






			def 					UpperCAmelCase							(self  :	int			)       ->	dict:





				"""simple docstring"""


				return {
				    "decay": self.decay,
				    "min_decay": self.min_decay,
				    "optimization_step": self.optimization_step,
				    "update_after_step": self.update_after_step,
				    "use_ema_warmup": self.use_ema_warmup,
				    "inv_gamma": self.inv_gamma,
				    "power": self.power,
				    "shadow_params": self.shadow_params,
				}






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Iterable[torch.nn.Parameter]			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  [param.detach().cpu().clone() for param in parameters]






			def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	Iterable[torch.nn.Parameter]			)       ->	None:





				"""simple docstring"""


				if self.temp_stored_params is None:
					raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''			)
				for c_param, param in zip(self.temp_stored_params      ,SCREAMING_SNAKE_CASE_			):
					param.data.copy_(c_param.data			)

				# Better memory-wise.
				lowerCAmelCase												=  None






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	dict			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  copy.deepcopy(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  state_dict.get('''decay'''      ,self.decay			)
				if self.decay < 0.0 or self.decay > 1.0:
					raise ValueError('''Decay must be between 0 and 1'''			)

				lowerCAmelCase												=  state_dict.get('''min_decay'''      ,self.min_decay			)
				if not isinstance(self.min_decay      ,SCREAMING_SNAKE_CASE_			):
					raise ValueError('''Invalid min_decay'''			)

				lowerCAmelCase												=  state_dict.get('''optimization_step'''      ,self.optimization_step			)
				if not isinstance(self.optimization_step      ,SCREAMING_SNAKE_CASE_			):
					raise ValueError('''Invalid optimization_step'''			)

				lowerCAmelCase												=  state_dict.get('''update_after_step'''      ,self.update_after_step			)
				if not isinstance(self.update_after_step      ,SCREAMING_SNAKE_CASE_			):
					raise ValueError('''Invalid update_after_step'''			)

				lowerCAmelCase												=  state_dict.get('''use_ema_warmup'''      ,self.use_ema_warmup			)
				if not isinstance(self.use_ema_warmup      ,SCREAMING_SNAKE_CASE_			):
					raise ValueError('''Invalid use_ema_warmup'''			)

				lowerCAmelCase												=  state_dict.get('''inv_gamma'''      ,self.inv_gamma			)
				if not isinstance(self.inv_gamma      ,(float, int)			):
					raise ValueError('''Invalid inv_gamma'''			)

				lowerCAmelCase												=  state_dict.get('''power'''      ,self.power			)
				if not isinstance(self.power      ,(float, int)			):
					raise ValueError('''Invalid power'''			)

				lowerCAmelCase												=  state_dict.get('''shadow_params'''      ,SCREAMING_SNAKE_CASE_			)
				if shadow_params is not None:
					lowerCAmelCase												=  shadow_params
					if not isinstance(self.shadow_params      ,SCREAMING_SNAKE_CASE_			):
						raise ValueError('''shadow_params must be a list'''			)
					if not all(isinstance(SCREAMING_SNAKE_CASE_      ,torch.Tensor			) for p in self.shadow_params			):
						raise ValueError('''shadow_params must all be Tensors'''			)




"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


